The presence of blood-brain barrier (BBB) dysfunction in patients with Alzheimer’s disease (AD) is unclear. This study uses a novel MRI approach to study AD and age-related alterations in BBB leakage to water (PSw) and gadolinium (Ktrans) in a transgenic rat model of AD. We show PSw is increased in transgenic animals relative to wild-types but that Ktrans is independent of genotype. This study demonstrates the benefit of probing the BBB with molecules of different sizes, and suggests measurements of BBB permeability to water are more sensitive to AD-related BBB alterations than estimates of gadolinium leakage.
Two cohorts of TgF344-AD rats were scanned on a 7T Bruker BioSpec system (Bruker Corporation, Billerica, USA): 5 transgenic (TG) and 5 wildtype (WT) aged 13 months, and 8TG/5WT aged 18 months. The young cohort were scanned twice (Δt = 1.5 weeks) to estimate scan-rescan reproducibility of MRI measures. All experiments were carried out in accordance with the Animal Scientific Procedures act 1986 and approved by the University of Manchester Local Ethical Review Committee.
The MFAME-MRI protocol is shown in Figure 1. Dataset A was collected for brain region delineation, CSF masking, and lesion detection. Dataset B enabled combined measurement of B1+ and native T1. Datasets C and E were collected for estimation of Ktrans and were designed to have short TR and high spatial resolution, to minimise both sensitivity to transvascular water exchange in tissue and partial volume effects in the superior sagittal sinus (SSS), respectively. Dataset D was collected to estimate PSw, and designed with long TR, low spatial resolution, and multiple flip angles, each optimised to maximize sensitivity to transvascular water exchange. All rats were anesthetized with 4% isoflurane and maintained with 2.5% isoflurane in 100% O2. Gd-DOTA (0.5 mmol.kg-1) was injected intravenously with a pump at 1 mL.min-1 on the 6th volume of dataset C. The data analysis pipeline is shown in Figure 2. Ktrans was estimated by fitting the Patlak model to datasets C and E. PSw was estimated by fitting a 2-site one-exchange model7 to dataset D. The effect of genotype, age, and brain region (plus interaction terms) on PSw and Ktrans were estimated using three-way ANOVA. Post-hoc tests on TG/WT differences, corrected for multiple comparisons, were undertaken using Tukey’s Honest Significant Differences (HSD) method. All model fitting and statistical analyses were performed in R (Version 3.1, Austria).
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